CLIO Pandas

io.github.iowarp/pandas-mcp

Documentation

Pandas MCP Server

Part of CLIO Kit - Gnosis Research Center

Pandas MCP is a Model Context Protocol server that enables LLMs to perform advanced data analysis and manipulation using the powerful Pandas library, featuring comprehensive statistical analysis, data cleaning and transformation, time series operations, multi-format data I/O (CSV, Excel, JSON, Pa...

Quick Start

uvx clio-kit mcp-server pandas

Documentation

Full Documentation: CLIO Kit Website
Installation Guide: See INSTALLATION.md
Contributing: See Contribution Guide

Description

🛠️ Installation

Requirements

Python 3.10 or higher
uv package manager (recommended)

Install in Cursor

Go to: Settings -> Cursor Settings -> MCP -> Add new global MCP server

Pasting the following configuration into your Cursor ~/.cursor/mcp.json file is the recommended approach. You may also install in a specific project by creating .cursor/mcp.json in your project folder. See Cursor MCP docs for more info.

{
  "mcpServers": {
    "pandas-mcp": {
      "command": "uvx",
      "args": ["clio-kit", "mcp-server", "pandas"]
    }
  }
}

Install in VS Code

Add this to your VS Code MCP config file. See VS Code MCP docs for more info.

"mcp": {
  "servers": {
    "pandas-mcp": {
      "type": "stdio",
      "command": "uvx",
      "args": ["clio-kit", "mcp-server", "pandas"]
    }
  }
}

Install in Claude Code

Run this command. See Claude Code MCP docs for more info.

claude mcp add pandas-mcp -- uvx clio-kit mcp-server pandas

Install in Claude Desktop

Add this to your Claude Desktop claude_desktop_config.json file. See Claude Desktop MCP docs for more info.

{
  "mcpServers": {
    "pandas-mcp": {
      "command": "uvx",
      "args": ["clio-kit", "mcp-server", "pandas"]
    }
  }
}

Manual Setup

Linux/macOS:

CLONE_DIR=$(pwd)
git clone https://github.com/iowarp/clio-kit.git
uv --directory=$CLONE_DIR/clio-kit/clio-kit-mcp-servers/pandas run pandas-mcp --help

Windows CMD:

set CLONE_DIR=%cd%
git clone https://github.com/iowarp/clio-kit.git
uv --directory=%CLONE_DIR%\clio-kit\clio-kit-mcp-servers\pandas run pandas-mcp --help

Windows PowerShell:

$env:CLONE_DIR=$PWD
git clone https://github.com/iowarp/clio-kit.git
uv --directory=$env:CLONE_DIR\clio-kit\clio-kit-mcp-servers\pandas run pandas-mcp --help

Capabilities

`load_data`

Description: Load and parse data from CSV, Excel, JSON, Parquet, or HDF5 files with optional column selection and row limiting. Hints: read-only, idempotent Tags: data-analysis, io

`save_data`

Description: Save data to CSV, Excel, JSON, Parquet, or HDF5 with auto-detected format and optional index inclusion. Hints: idempotent Tags: data-analysis, io

`statistical_summary`

Description: Compute descriptive statistics, distribution analysis, and outlier detection for numerical and categorical columns. Hints: read-only, idempotent Tags: data-analysis, statistics

`correlation_analysis`

Description: Compute correlation matrices (Pearson, Spearman, or Kendall) with significance testing and strong-correlation detection. Hints: read-only, idempotent Tags: data-analysis, statistics

`hypothesis_testing`

Description: Run statistical hypothesis tests (t-test, chi-square, ANOVA, normality, Mann-Whitney) with p-values and effect sizes. Hints: read-only, idempotent Tags: data-analysis, statistics

`handle_missing_data`

Description: Detect, impute, or remove missing values using strategies like mean/median/mode fill, forward/backward fill, or interpolation. Hints: read-only, idempotent Tags: cleaning, data-analysis

`clean_data`

Description: Remove duplicates, detect outliers via IQR/Z-score, and optimize data types in a single pass. Hints: read-only, idempotent Tags: cleaning, data-analysis

`groupby_operations`

Description: Group data by columns and apply aggregations (sum, mean, count, min, max, std, median) with optional pre-filter. Hints: read-only, idempotent Tags: data-analysis, transformation

`merge_datasets`

Description: Join two datasets using inner, outer, left, or right joins on specified key columns. Hints: read-only, idempotent Tags: data-analysis, transformation

`pivot_table`

Description: Create pivot tables with configurable row index, column headers, value columns, and aggregation function. Hints: read-only, idempotent Tags: data-analysis, transformation

`time_series_operations`

Description: Resample, compute rolling statistics, create lag features, or difference a time series. Hints: read-only, idempotent Tags: data-analysis, time-series

`validate_data`

Description: Validate columns against rules for min/max range, data type, nullability, uniqueness, and regex patterns. Hints: read-only, idempotent Tags: data-analysis, validation

`filter_data`

Description: Filter rows using comparison, membership, pattern-matching, and null-check operators across multiple columns. Hints: read-only, idempotent Tags: data-analysis, filtering

`optimize_memory`

Description: Analyze and reduce DataFrame memory usage through automatic dtype optimization and chunked-processing recommendations. Hints: read-only, idempotent Tags: data-analysis, optimization

`profile_data`

Description: Generate a full dataset profile: shape, types, missing values, distributions, quality checks, and optional correlations. Hints: read-only, idempotent Tags: data-analysis, profiling

`profile_csv`

Description: Quickly profile a CSV file: row/column counts, per-column dtype, null counts, and min/max/mean for numeric columns. Hints: read-only, idempotent Tags: csv, data-analysis, profiling

Resources

pandas://capabilities - Supported pandas operations and file formats.

Prompts

analyze_dataset: Guided workflow for exploring and analyzing a dataset.

Claude Code

claude mcp add clio-pandas -- uvx clio-kit pandas

Or install via the CLIO Kit plugin marketplace:

/plugin marketplace add iowarp/clio-kit
/plugin install clio-pandas@iowarp-clio-kit

Claude Desktop

Add to your Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "clio-pandas": {
      "command": "uvx",
      "args": [
        "clio-kit",
        "pandas"
      ]
    }
  }
}

Gemini CLI

Add to ~/.gemini/settings.json:

{
  "mcpServers": {
    "clio-pandas": {
      "command": "uvx",
      "args": [
        "clio-kit",
        "pandas"
      ]
    }
  }
}

Or install the CLIO Kit extension:

gemini extensions install https://github.com/iowarp/clio-kit

Examples

1. Data Loading and Profiling

I have a large CSV file with sales data that I need to load and get a comprehensive profile including data types, missing values, and basic statistics.

Tools called:

load_data - Load CSV file with intelligent format detection
profile_data - Get comprehensive data profile and quality metrics
statistical_summary - Generate descriptive statistics and distributions

2. Data Cleaning and Quality Assessment

My dataset has missing values and outliers that need to be handled. I also want to remove duplicates and validate the data quality.

Tools called:

handle_missing_data - Impute missing values with appropriate strategies
clean_data - Remove outliers, duplicates, and optimize data types
validate_data - Apply business rules and data quality checks

3. Statistical Analysis and Correlation

Analyze the relationships between different variables in my dataset and perform hypothesis testing to validate my assumptions.

Tools called:

correlation_analysis - Calculate correlation matrices with different methods
hypothesis_testing - Perform t-tests, ANOVA, and normality tests
statistical_summary - Generate comprehensive statistical insights

4. Data Transformation and Aggregation

I need to group my sales data by region and product category, then create pivot tables for cross-analysis and merge with customer data.

Tools called:

groupby_operations - Group data and perform multiple aggregations
pivot_table - Create pivot tables with multi-level indexing
merge_datasets - Join datasets using different merge strategies

5. Time Series Analysis and Filtering

Analyze my time series data by resampling to different frequencies, calculating rolling averages, and filtering specific date ranges.

Tools called:

time_series_operations - Resample, rolling windows, and lag features
filter_data - Apply complex time-based filtering conditions
statistical_summary - Analyze time series patterns and trends

6. Data Export and Memory Optimization

Optimize memory usage of my large dataset and export the cleaned data to multiple formats for different teams.

Tools called:

optimize_memory - Reduce memory usage with dtype optimization
save_data - Export to CSV, Excel, Parquet, and JSON formats
profile_data - Verify optimization results and final data quality